Microelectromechanical system (MEMS) sensors have recently been integrated within mobile devices to provide acceleration measurements for identifying motion. Absolute stationarity of the mobile device (e.g., sitting unattended on a nightstand or desk), for example, may be inferred from triaxial MEMS accelerometer signals when the acceleration change measured on all axes is insignificant. Recent advancements have also enabled the identification of other stationarity situations. A “fidgeting” motion (e.g., when the device is attached to a person's belt while sitting in a meeting or being held in a person's hand while standing conversing with a colleague), for example, may be distinguished from other types of pedestrial motion such as continuous walking or running.
However, conventional stationarity detection schemes (at least those operating efficiently with low power consumption) have been heretofore limited to only relative stationarity identification with respect to a given inertial frame of reference. They have not been able to efficiently identify stationarity of a mobile device with respect to a given geographic location on Earth. For a wide number of context-aware applications, there is a need in the art to be able to efficiently identify geographic stationarity of a mobile device.